This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
Load the required libraries. If you don’t have them installed, please do by running install.packages()
library(plotly)
library(stringr)
library(reshape2)
library(dplyr)
library(readr)
Load the NMR binned csv. Just adapt the path to location of your file. You can use autocompletion using the tab key
Binning_Fusarium_sh1 <- read_csv("../../data/Binning_Fusarium_matz_center.csv")Duplicated column names deduplicated: '-115.03' => '-115.03_1' [146], '702.028' => '702.028_1' [283], '1675.59' => '1675.59_1' [286], '-314.082' => '-314.082_1' [308], '-402.228' => '-402.228_1' [326], '-202.469' => '-202.469_1' [331], '-240.71' => '-240.71_1' [495], '313.821' => '313.821_1' [599], '443.177' => '443.177_1' [614], '-168.964' => '-168.964_1' [628], '-161.33' => '-161.33_1' [637], '-305.105' => '-305.105_1' [661], '65.8555' => '65.8555_1' [730], '281.871' => '281.871_1' [735], '785.014' => '785.014_1' [778], '221.152' => '221.152_1' [803], '141.064' => '141.064_1' [830], '-195.684' => '-195.684_1' [865], '146.084' => '146.084_1' [874], '-49.4336' => '-49.4336_1' [877], '96.0381' => '96.0381_1' [903], '-4.61914' => '-4.61914_1' [921], '-40.5273' => '-40.5273_1' [923], '-57.3506' => '-57.3506_1' [963], '-77.3545' => '-77.3545_1' [965], '-135.034' => '-135.034_1' [982], '-121.533' => '-121.533_1' [986], '-135.883' => '-135.883_1' [1002], '-168.964' => '-168.964_2' [1039], '-21.3008' => '-21.3008_1' [1041], '11.0029' => '11.0029_1' [1055], '134.562' => '134.562_1' [1058], '-200.207' => '-200.207_1' [1066], '52.9902' => '52.9902_1' [1077], '58.3623' => '58.3623_1' [1099], '-0.519531' => '-0.519531_1' [1131], '-130.652' => '-130.652_1' [1138], '158.453' => '158.453_1' [1140], '166.3' => '166.3_1' [1153], '92.4326' => '92.4326_1' [1156], '-10.7686' => '-10.7686_1' [1160], '-36.7812' => '-36.7812_1' [1166], '131.098' => '131.098_1' [1174], '-150.373' => '-150.373_1' [1179], '149.265' => '149.265_1' [1190], '47.6885' => '47.6885_1' [1216], '10.7197' => '10.7197_1' [1225], '-60.6729' => '-60.6729_1' [1232], '-268.419' => '-268.419_1' [1236], '24.998' => '24.998_1' [1253], '-96.9346' => '-96.9346_1' [1264], '207.086' => '207.086_1' [1265], '-87.4629' => '-87.4629_1' [1276], '105.51' => '105.51_1' [1285], '50.2334' => '50.2334_1' [1288], '56.2412' => '56.2412_1' [1293], '-26.1777' => '-26.1777_1' [1314], '-229.471' => '-229.471_1' [1318], '70.3086' => '70.3086_1' [1333], '-63.8535' => '-63.8535_1' [1342], '28.8857' => '28.8857_1' [1343], '86.5664' => '86.5664_1' [1345], '8.95312' => '8.95312_1' [1353], '43.8008' => '43.8008_1' [1354], '-5.4668' => '-5.4668_1' [1355], '-4.61914' => '-4.61914_2' [1361], '62.6035' => '62.6035_1' [1388], '75.8213' => '75.8213_1' [1395], '-67.2471' => '-67.2471_1' [1400], '22.666' => '22.666_1' [1410], '104.309' => '104.309_1' [1414], '19.9795' => '19.9795_1' [1425], '26.3418' => '26.3418_1' [1428], '-10.415' => '-10.415_1' [1431], '-75.5879' => '-75.5879_1' [1440], '-55.584' => '-55.584_1' [1441], '4.64062' => '4.64062_1' [1444], '52.9902' => '52.9902_2' [1456], '-85.625' => '-85.625_1' [1457], '-266.369' => '-266.369_1' [1459], '-126.199' => '-126.199_1' [1472], '65.502' => '65.502_1' [1506], '-126.128' => '-126.128_1' [1508], '-74.7393' => '-74.7393_1' [1523], '-8.29492' => '-8.29492_1' [1540], '-82.0908' => '-82.0908_1' [1544], '-103.721' => '-103.721_1' [1554], '-19.958' => '-19.958_1' [1558], '18.3545' => '18.3545_1' [1572], '-116.373' => '-116.373_1' [1578], '827.072' => '827.072_1' [1602]
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
.default = col_double()
)
ℹ Use `spec()` for the full column specifications.Lets have a look at the first rows of this file
head(Binning_Fusarium_sh1)OK. Be sure to have ppm on the columns and fraction numbers as rows. Now we transform the dataframe as a matrix
DTz <- as.matrix(data.frame(Binning_Fusarium_sh1))Lets have a look at the structure of the file
str(DTz) num [1:134, 1:1602] -12215 -13266 -12792 -12116 -12881 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:1602] "X.14393.5" "X358.495" "X.14208.8" "X602.433" ...Now we’ll remove the row indexes
DTz <- DTz[,-1] And we set the matrix row and colnames according to the ones of the df
colnames(DTz) <- colnames(Binning_Fusarium_sh1)[-1]
rownames(DTz) <- rownames(Binning_Fusarium_sh1)
Let’s transform these data in the long form
mtrx.melt <- melt(DTz, id.vars = c('sample', 'ppm'), measure.vars = 'int')
names(mtrx.melt) <- c('sample', 'ppm', 'int')
Now we can plot a quick 3Dplot to have an overview of the data
p <- plot_ly(z = ~DTz) %>% add_surface()
pOK so now we want to remove the annoying signals corresponding to the solvents. We can check at the colunms name and note their numbers. Example its DMSO > signals at 2.5 ppm I want to delete columns 350,351 and 352.
colnames(DTz) [1] "358.495" "-14208.8" "602.433" "-953.72" "-696.635" "-713.104" "-655.849" "-586.647" "-556.252"
[10] "-456.443" "-367.592" "-187.201" "-35.2256" "14.4658" "-106.619" "358.354" "129.261" "339.622"
[19] "187.93" "383.023" "424.374" "402.179" "342.52" "118.799" "165.169" "534.361" "473.147"
[28] "100.421" "379.064" "231.26" "382.67" "390.162" "368.179" "527.223" "493.435" "601.23"
[37] "27.0488" "401.118" "463.322" "272.895" "454.628" "358.637" "478.591" "144.246" "303.572"
[46] "444.52" "131.098" "138.874" "215.568" "112.366" "222.071" "175.843" "208.217" "67.127"
[55] "175.983" "276.287" "389.385" "260.807" "153.576" "134.562" "65.8555" "200.229" "341.389"
[64] "118.092" "416.599" "213.659" "-26.1777" "-19.4629" "215.427" "214.296" "81.4766" "-25.4717"
[73] "55.8174" "-95.0967" "138.096" "81.6182" "313.821" "35.248" "64.0176" "3.50977" "182.274"
[82] "8.1748" "187.223" "74.1963" "26.9072" "160.928" "139.651" "-67.5293" "85.2227" "134.42"
[91] "129.967" "-126.199" "-7.58789" "-163.804" "235.36" "15.8799" "50.5166" "98.3701" "83.668"
[100] "-37.6289" "-328.148" "-126.411" "-33.1055" "-112.839" "-40.5273" "22.3125" "-103.297" "43.8008"
[109] "-217.879" "75.1855" "-106.76" "-161.188" "10.5781" "-190.24" "-218.444" "-10.9102" "12.1338"
[118] "-67.1055" "-1.93262" "-115.03" "-182.677" "-282.697" "-295.421" "-29.2178" "-137.509" "-132.561"
[127] "-177.022" "-5.4668" "-43.3545" "-114.96" "-135.388" "-185.434" "7.18555" "-188.968" "-176.244"
[136] "-231.309" "-255.201" "-148.183" "-16.4941" "-171.367" "-18.6855" "-222.402" "-103.014" "-202.469"
[145] "-115.03_1" "-161.684" "-252.373" "-80.1826" "-398.481" "-320.231" "-280.789" "-390.141" "-185.646"
[154] "-305.105" "-216.041" "-293.229" "-293.583" "-201.196" "-419.97" "-213.354" "16.5166" "-260.573"
[163] "-82.2324" "-274.074" "-260.36" "-207.912" "-412.053" "-361.725" "-345.679" "-240.71" "-237.671"
[172] "-270.964" "-179.284" "-402.228" "-68.3066" "-473.055" "-346.881" "-147.546" "-222.897" "-375.014"
[181] "-325.392" "-122.17" "-234.631" "-163.168" "16.1631" "-125.28" "-62.1572" "37.0146" "238.611"
[190] "240.449" "220.375" "-268.419" "-364.976" "-150.373" "-301.005" "-95.6621" "26.3418" "-14.0205"
[199] "-138.145" "11.2852" "207.086" "-256.332" "-63.8535" "-296.905" "224.263" "1341.88" "548.146"
[208] "5301.35" "5063.42" "504.744" "785.014" "1032.34" "2084.08" "1927.37" "1625.75" "2876.54"
[217] "3343.21" "2368.95" "942.149" "435.189" "449.114" "456.819" "425.646" "1371.99" "3039.19"
[226] "1787.41" "3315.29" "5336.48" "2003.22" "1695.31" "933.172" "1047.54" "536.199" "371.431"
[235] "388.678" "87.6973" "-13.9492" "394.757" "576.915" "44.3662" "-174.194" "141.56" "4278.17"
[244] "-44.1328" "-158.715" "-246.365" "-195.612" "58.2207" "-113.829" "-77.3545" "15.0322" "264.482"
[253] "160.786" "177.397" "336.724" "116.184" "186.728" "-200.065" "-396.148" "-254.564" "-195.4"
[262] "173.156" "-237.176" "-279.729" "-324.826" "-101.529" "212.316" "48.5371" "-82.0908" "-22.7852"
[271] "-255.13" "-368.581" "-30.9141" "-538.935" "-279.517" "-169.388" "-275.629" "-353.808" "-3.7002"
[280] "42.8115" "702.028" "702.028_1" "443.177" "1675.59" "1675.59_1" "3295.85" "876.341" "582.146"
[289] "104.309" "82.6074" "-288.211" "-133.196" "-173.982" "-404.065" "-125.351" "-367.309" "-435.662"
[298] "-228.835" "-196.249" "-209.325" "-294.714" "1009.94" "-382.931" "674.249" "-253.363" "-314.082"
[307] "-314.082_1" "-392.826" "-453.687" "-438.772" "-55.3711" "205.106" "-313.375" "102.965" "-49.4336"
[316] "-21.3008" "-333.238" "-133.55" "-625.312" "-491.15" "-492.493" "-322.776" "-440.539" "-321.504"
[325] "-402.228_1" "-431.916" "-122.735" "-474.186" "-516.88" "-202.469_1" "-203.741" "-32.3281" "539.31"
[334] "-479.487" "20389" "613.671" "1259.53" "-1378.61" "-415.729" "460.07" "1321.87" "45321.9"
[343] "43390.2" "5332.88" "10040.9" "28657.8" "19250.2" "8769" "8787.37" "14597.5" "29674.7"
[352] "102524" "757484" "1.90E+07" "1.18E+06" "243020" "51894.9" "31236" "16133.4" "10413.5"
[361] "9314.48" "12067.1" "2887.78" "4589.76" "94192.6" "23062.5" "3709.29" "2056.02" "2069.94"
[370] "2199.86" "466.433" "978.765" "1028.95" "1126.99" "497.888" "511.247" "869.483" "625.829"
[379] "810.391" "960.527" "804.382" "1169.62" "986.822" "234.865" "547.227" "166.3" "220.233"
[388] "201.147" "244.974" "202.208" "175.206" "122.051" "354.749" "-44.2734" "187.294" "49.8096"
[397] "2.37891" "320.042" "1342.58" "8941.33" "374.541" "237.41" "29.0273" "445.722" "566.807"
[406] "453.779" "519.164" "454.274" "678.49" "496.615" "1070.02" "583.135" "1236.49" "2219.8"
[415] "7940.13" "4900.78" "965.192" "1429.6" "1358.49" "2152.93" "4612.59" "1993.96" "1921.15"
[424] "2810.38" "3361.09" "4272.38" "5766.89" "5625.09" "8655.61" "12033.3" "20393" "33175.1"
[433] "61905.3" "152525" "1.02E+06" "1.46E+06" "166382" "60913.6" "35899.1" "21488.9" "14860.4"
[442] "10017.4" "8240.55" "6283.18" "6664.39" "5805.91" "4730.49" "3903.4" "3366.47" "3749.51"
[451] "6450.78" "9137.55" "7284.45" "4554.34" "3685.05" "3187.7" "4872.08" "4623.83" "5808.74"
[460] "4348.36" "3160.49" "3705.55" "3552.3" "4774.6" "4690.84" "3299.1" "2635.29" "2124.16"
[469] "3312.32" "2817.24" "2975.64" "2190.11" "1752.49" "1780.2" "1352.62" "2181.84" "1490.81"
[478] "2415.25" "1979.75" "2486.85" "1079.7" "928.224" "635.584" "645.621" "294.029" "1018.91"
[487] "3302.57" "1729.1" "528.989" "92.4326" "309.792" "591.971" "180.225" "-240.71_1" "147.709"
[496] "213.589" "-230.249" "420.91" "4956.76" "2161.48" "170.471" "15.7383" "5.91309" "1053.2"
[505] "2762.24" "1944.41" "212.67" "-65.9746" "-134.822" "-144.012" "281.871" "1071.93" "662.232"
[514] "285.052" "52.4951" "971.555" "2295.57" "1846.15" "0.258789" "-119.342" "76.2461" "649.438"
[523] "1358.14" "358.849" "58.3623" "-59.1885" "-59.9658" "57.8672" "86.7783" "216.486" "180.437"
[532] "-141.325" "239.46" "519.729" "746.844" "585.68" "743.451" "870.05" "1033.69" "3647.23"
[541] "6732.18" "4759.83" "1569.49" "1024.22" "811.31" "827.991" "1163.33" "3082.59" "3521.62"
[550] "895.214" "509.339" "689.8" "150.325" "96.0381" "310.711" "232.815" "400.978" "132.653"
[559] "18.9199" "-34.8018" "141.064" "656.295" "690.719" "1531.81" "1279.82" "409.035" "524.466"
[568] "208.57" "946.531" "2024.78" "3222.97" "2476.95" "258.403" "152.304" "44.6494" "186.516"
[577] "154.354" "225.464" "143.468" "148.981" "261.584" "-4.61914" "169.764" "341.247" "600.1"
[586] "601.867" "221.152" "19.7676" "172.308" "-180.839" "-49.0098" "-268.489" "-252.444" "213.377"
[595] "-160.34" "-402.439" "-53.3926" "313.821_1" "-56.502" "-38.4072" "-135.883" "-456.797" "-132.277"
[604] "-95.0264" "-185.01" "116.325" "187.081" "115.83" "103.318" "2187.71" "4368.23" "986.187"
[613] "443.177_1" "329.867" "97.2393" "-168.964" "91.0898" "29.8047" "560.939" "1628.58" "307.601"
[622] "-264.814" "-161.33" "-36.4277" "-116.373" "696.232" "-168.964_1" "-129.309" "-427.745" "-324.332"
[631] "-98.7021" "56.2412" "-150.586" "-101.741" "-227.562" "-161.33_1" "-242.194" "-195.684" "1742.31"
[640] "1608.15" "716.52" "1171.03" "2055.81" "1557.68" "1317.7" "8813.17" "2267.93" "-130.652"
[649] "-333.874" "-376.215" "-273.791" "-407.67" "-413.396" "-656.344" "-491.574" "-314.647" "-120.049"
[658] "-245.658" "-317.546" "-305.105_1" "-382.012" "-447.184" "-126.128" "-187.413" "8.74023" "-158.148"
[667] "998.769" "-20.3115" "-374.165" "-456.655" "-266.369" "-39.3965" "-307.861" "-252.091" "-302.207"
[676] "-212.365" "-172.64" "-139.771" "415.256" "1755.18" "565.676" "-154.261" "-55.8662" "-305.953"
[685] "-526.564" "-29.4297" "-525.646" "-156.877" "2170.53" "2577.54" "-167.268" "-336.984" "-199.712"
[694] "-367.662" "-478.427" "-370.772" "-534.552" "-194.481" "-389.857" "-419.334" "-210.244" "-200.207"
[703] "-212.224" "47.6885" "-432.41" "-296.41" "-337.126" "-198.864" "-268.914" "-148.253" "-381.021"
[712] "-215.829" "-283.192" "-67.0342" "38.0049" "25.7764" "-212.082" "-110.224" "11.0029" "-355.504"
[721] "81.123" "-232.652" "-346.668" "-149.879" "-152.282" "173.58" "-36.71" "99.7842" "65.8555_1"
[730] "190.828" "454.84" "162.129" "38.5703" "281.871_1" "-93.4717" "457.313" "555.002" "708.603"
[739] "417.729" "187.718" "305.057" "158.312" "-72.9014" "23.5137" "157.817" "290" "441.551"
[748] "1382.1" "175.277" "224.05" "461.838" "494.354" "152.516" "217.265" "381.326" "682.236"
[757] "370.865" "422.042" "17.7881" "64.9365" "283.073" "137.46" "289.576" "176.479" "164.108"
[766] "213.801" "199.805" "492.445" "486.86" "696.374" "511.459" "629.293" "498.807" "625.547"
[775] "582.641" "570.623" "785.014_1" "705.775" "909.28" "747.339" "694.678" "815.833" "542.35"
[784] "244.479" "371.854" "193.16" "-51.2715" "59.1396" "-140.689" "938.049" "623.497" "200.865"
[793] "6.76172" "105.51" "348.245" "264.978" "258.475" "39.3477" "213.447" "242.217" "396.241"
[802] "221.152_1" "430.736" "1800.98" "432.361" "181.215" "281.518" "736.947" "473.854" "623.709"
[811] "1961.87" "671.917" "-57.2803" "95.6143" "100.986" "58.4326" "124.383" "65.502" "173.015"
[820] "176.267" "398.009" "76.9531" "21.6758" "113.073" "107.701" "64.2295" "50.2334" "258.263"
[829] "141.064_1" "433.775" "1577.19" "1939.1" "1895.99" "2033.47" "5548.68" "1346.05" "528.282"
[838] "326.898" "560.586" "1979.89" "10791.3" "-50.4238" "196.129" "62.6035" "77.165" "146.084"
[847] "-264.531" "-17.8369" "-215.122" "-24.835" "-227.351" "-229.471" "-192.644" "-113.617" "-66.2568"
[856] "-128.743" "-339.883" "-117.575" "-141.396" "-1.5791" "-34.166" "18.4248" "-185.504" "-195.684_1"
[865] "86.5664" "-154.12" "149.265" "-13.3135" "50.7285" "124.171" "-99.4795" "-15.0098" "146.084_1"
[874] "-154.474" "-60.6729" "-49.4336_1" "-70.8516" "-62.6523" "-172.286" "-37.417" "-173.417" "-164.511"
[883] "-362.644" "-172.428" "-204.519" "-135.034" "259.817" "49.5264" "1179.65" "2135.05" "241.58"
[892] "21.3936" "301.381" "104.661" "6.5498" "201.431" "410.661" "648.802" "388.748" "223.556"
[901] "282.437" "96.0381_1" "276.782" "-100.61" "-181.97" "259.676" "1392.14" "243.56" "359.626"
[910] "379.913" "1642.08" "1792.15" "1393.27" "1327.32" "1154.14" "390.516" "-2.56934" "53.2725"
[919] "-162.319" "-4.61914_1" "-104.993" "-40.5273_1" "79.0029" "-179.496" "72.8525" "-136.448" "-291.038"
[928] "-8.50684" "130.392" "135.057" "489.335" "420.062" "828.769" "2038.56" "6233.28" "6566.77"
[937] "1049.52" "227.302" "90.5947" "-19.958" "7.53906" "-55.5127" "-238.66" "-195.117" "-103.721"
[946] "22.666" "-16.5645" "182.981" "286.254" "1721.75" "1833.85" "896.345" "327.04" "-130.723"
[955] "128.129" "-90.6436" "-5.82031" "-161.612" "-57.3506" "-75.5879" "-249.334" "-57.3506_1" "-308.074"
[964] "-77.3545_1" "-302.843" "-190.452" "-260.502" "-153.342" "-63.7832" "-109.163" "-121.533" "-381.799"
[973] "-166.914" "-390.847" "-124.785" "-96.0869" "-111.284" "-231.238" "50.8691" "-117.929" "-135.034_1"
[982] "-36.2861" "-93.3301" "-179.426" "-121.533_1" "-340.165" "-302.631" "-163.945" "-70.0039" "-117.292"
[991] "-75.6582" "-217.454" "-221.13" "-33.459" "-68.3779" "492.304" "3623.2" "216.346" "-76.9307"
[1000] "-36.7812"
[ reached getOption("max.print") -- omitted 601 entries ]
DTzred <- DTz[,-348:-352]
Now lets plot again …. Does it looks better ?
p <- plot_ly(z = ~DTzred) %>% add_surface()
pElse repeat the previous step For this execute the following code. Be sure to run it on the previously cleaned object. In this case DTzred Be sure to check again the columns numbers sinsce these have changed
colnames(DTzred)
DTzred <- DTzred[,-348:-352]Now that you have the cleaned data object lets have a look at the 2d map. Be patient, this one is longer to plot.
p <- plot_ly(mtrx.melt, x = ~sample, y = ~ppm, z = ~int, type = "contour",
colors = 'YlOrRd',
autocontour = F,
contours = list(
start = 10000,
end = 1200000,
size = 5000
)
)
pNAIf you want to plot the map with ppm on the x-axis just reverse the axis order. Play with start value (to fix the noise) and size value to fix the contour space. Change color if you wish by changing the color field. For more info on 2d contour plot with plotly check https://plot.ly/r/contour-plots/
p <- plot_ly(mtrx.melt, x = ~ppm, y = ~sample, z = ~int, type = "contour",
autocontour = F,
colors = 'YlOrRd',
contours = list(
start = 10000,
end = 1200000,
size = 50000
)
) %>% layout(xaxis = list(autorange = "reversed"))
p